The Effect of Robot Attentional Behaviors on User Perceptions and Behaviors in a Simulated Health Care Interaction: Randomized Controlled Trial.


Journal

Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882

Informations de publication

Date de publication:
04 10 2019
Historique:
received: 09 02 2019
accepted: 17 07 2019
revised: 06 05 2019
entrez: 8 10 2019
pubmed: 8 10 2019
medline: 12 6 2020
Statut: epublish

Résumé

For robots to be effectively used in health applications, they need to display appropriate social behaviors. A fundamental requirement in all social interactions is the ability to engage, maintain, and demonstrate attention. Attentional behaviors include leaning forward, self-disclosure, and changes in voice pitch. This study aimed to examine the effect of robot attentional behaviors on user perceptions and behaviors in a simulated health care interaction. A parallel randomized controlled trial with a 1:1:1:1 allocation ratio was conducted. We randomized participants to 1 of 4 experimental conditions before engaging in a scripted face-to-face interaction with a fully automated medical receptionist robot. Experimental conditions included a self-disclosure condition, voice pitch change condition, forward lean condition, and neutral condition. Participants completed paper-based postinteraction measures relating to engagement, perceived robot attention, and perceived robot empathy. We video recorded interactions and coded for participant attentional behaviors. A total of 181 participants were recruited from the University of Auckland. Participants who interacted with the robot in the forward lean and self-disclosure conditions found the robot to be significantly more stimulating than those who interacted with the robot in the voice pitch or neutral conditions (P=.03). Participants in the forward lean, self-disclosure, and neutral conditions found the robot to be significantly more interesting than those in the voice pitch condition (P<.001). Participants in the forward lean and self-disclosure conditions spent significantly more time looking at the robot than participants in the neutral condition (P<.001). Significantly, more participants in the self-disclosure condition laughed during the interaction (P=.01), whereas significantly more participants in the forward lean condition leant toward the robot during the interaction (P<.001). The use of self-disclosure and forward lean by a health care robot can increase human engagement and attentional behaviors. Voice pitch changes did not increase attention or engagement. The small effects with regard to participant perceptions are potentially because of the limitations in self-report measures or a lack of comparison for most participants who had never interacted with a robot before. Further research could explore the use of self-disclosure and forward lean using a within-subjects design and in real health care settings.

Sections du résumé

BACKGROUND
For robots to be effectively used in health applications, they need to display appropriate social behaviors. A fundamental requirement in all social interactions is the ability to engage, maintain, and demonstrate attention. Attentional behaviors include leaning forward, self-disclosure, and changes in voice pitch.
OBJECTIVE
This study aimed to examine the effect of robot attentional behaviors on user perceptions and behaviors in a simulated health care interaction.
METHODS
A parallel randomized controlled trial with a 1:1:1:1 allocation ratio was conducted. We randomized participants to 1 of 4 experimental conditions before engaging in a scripted face-to-face interaction with a fully automated medical receptionist robot. Experimental conditions included a self-disclosure condition, voice pitch change condition, forward lean condition, and neutral condition. Participants completed paper-based postinteraction measures relating to engagement, perceived robot attention, and perceived robot empathy. We video recorded interactions and coded for participant attentional behaviors.
RESULTS
A total of 181 participants were recruited from the University of Auckland. Participants who interacted with the robot in the forward lean and self-disclosure conditions found the robot to be significantly more stimulating than those who interacted with the robot in the voice pitch or neutral conditions (P=.03). Participants in the forward lean, self-disclosure, and neutral conditions found the robot to be significantly more interesting than those in the voice pitch condition (P<.001). Participants in the forward lean and self-disclosure conditions spent significantly more time looking at the robot than participants in the neutral condition (P<.001). Significantly, more participants in the self-disclosure condition laughed during the interaction (P=.01), whereas significantly more participants in the forward lean condition leant toward the robot during the interaction (P<.001).
CONCLUSIONS
The use of self-disclosure and forward lean by a health care robot can increase human engagement and attentional behaviors. Voice pitch changes did not increase attention or engagement. The small effects with regard to participant perceptions are potentially because of the limitations in self-report measures or a lack of comparison for most participants who had never interacted with a robot before. Further research could explore the use of self-disclosure and forward lean using a within-subjects design and in real health care settings.

Identifiants

pubmed: 31588904
pii: v21i10e13667
doi: 10.2196/13667
pmc: PMC6914232
doi:

Types de publication

Journal Article Randomized Controlled Trial Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e13667

Commentaires et corrections

Type : ErratumIn

Informations de copyright

©Deborah L Johanson, Ho Seok Ahn, Bruce A MacDonald, Byeong Kyu Ahn, JongYoon Lim, Euijun Hwang, Craig J Sutherland, Elizabeth Broadbent. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.10.2019.

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Auteurs

Deborah L Johanson (DL)

Department of Psychological Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
Centre for Automation and Robotic Engineering Science, The University of Auckland, Auckland, New Zealand.

Ho Seok Ahn (HS)

Centre for Automation and Robotic Engineering Science, The University of Auckland, Auckland, New Zealand.
Department of Electrical, Computer, and Software Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand.

Bruce A MacDonald (BA)

Centre for Automation and Robotic Engineering Science, The University of Auckland, Auckland, New Zealand.
Department of Electrical, Computer, and Software Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand.

Byeong Kyu Ahn (BK)

Centre for Automation and Robotic Engineering Science, The University of Auckland, Auckland, New Zealand.
Department of Electrical, Computer, and Software Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand.

JongYoon Lim (J)

Centre for Automation and Robotic Engineering Science, The University of Auckland, Auckland, New Zealand.
Department of Electrical, Computer, and Software Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand.

Euijun Hwang (E)

Centre for Automation and Robotic Engineering Science, The University of Auckland, Auckland, New Zealand.
Department of Electrical, Computer, and Software Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand.

Craig J Sutherland (CJ)

Centre for Automation and Robotic Engineering Science, The University of Auckland, Auckland, New Zealand.
Department of Electrical, Computer, and Software Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand.

Elizabeth Broadbent (E)

Department of Psychological Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
Centre for Automation and Robotic Engineering Science, The University of Auckland, Auckland, New Zealand.

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Classifications MeSH